Overview

Dataset statistics

Number of variables16
Number of observations1266784
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory154.6 MiB
Average record size in memory128.0 B

Variable types

DateTime1
Text3
Categorical4
Numeric8

Alerts

DAY_CLS has constant value ""Constant
ETL_TYPE has constant value ""Constant
AVG_CAR_LEN is highly overall correlated with OCCUPY_RATE and 3 other fieldsHigh correlation
MD_TR_VOL is highly overall correlated with OCCUPY_RATE and 1 other fieldsHigh correlation
OCCUPY_RATE is highly overall correlated with AVG_CAR_LEN and 4 other fieldsHigh correlation
SM_TR_VOL is highly overall correlated with AVG_CAR_LEN and 3 other fieldsHigh correlation
TRVL_SPD is highly overall correlated with AVG_CAR_LEN and 3 other fieldsHigh correlation
TR_VOL is highly overall correlated with AVG_CAR_LEN and 4 other fieldsHigh correlation
DETR_FAIL_YN is highly imbalanced (> 99.9%)Imbalance
LG_TR_VOL is highly skewed (γ1 = 40.5592603)Skewed
TR_VOL has 630544 (49.8%) zerosZeros
SM_TR_VOL has 715634 (56.5%) zerosZeros
MD_TR_VOL has 1026404 (81.0%) zerosZeros
LG_TR_VOL has 1232613 (97.3%) zerosZeros
TRVL_SPD has 630544 (49.8%) zerosZeros
OCCUPY_RATE has 624936 (49.3%) zerosZeros
AVG_CAR_LEN has 633053 (50.0%) zerosZeros
AVG_CAR_TM has 430682 (34.0%) zerosZeros

Reproduction

Analysis started2024-03-26 00:37:06.655851
Analysis finished2024-03-26 00:38:18.719193
Duration1 minute and 12.06 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct80942
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
Minimum2020-01-19 00:00:00
Maximum2020-01-19 23:59:58
2024-03-26T09:38:18.818781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:18.971112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct443
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:19.134655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters11401056
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDET002905
2nd rowDET004902
3rd rowDET006102
4th rowDET003501
5th rowDET003503
ValueCountFrequency (%)
det010203 2880
 
0.2%
det000302 2880
 
0.2%
det002801 2880
 
0.2%
det005603 2880
 
0.2%
det002502 2880
 
0.2%
det002503 2880
 
0.2%
det012201 2880
 
0.2%
det002401 2880
 
0.2%
det002403 2880
 
0.2%
det012401 2880
 
0.2%
Other values (433) 1237984
97.7%
2024-03-26T09:38:19.501542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3813943
33.5%
D 1266784
 
11.1%
E 1266784
 
11.1%
T 1266784
 
11.1%
1 936704
 
8.2%
2 689036
 
6.0%
3 533594
 
4.7%
4 398401
 
3.5%
5 274831
 
2.4%
8 256009
 
2.2%
Other values (3) 698186
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11401056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3813943
33.5%
D 1266784
 
11.1%
E 1266784
 
11.1%
T 1266784
 
11.1%
1 936704
 
8.2%
2 689036
 
6.0%
3 533594
 
4.7%
4 398401
 
3.5%
5 274831
 
2.4%
8 256009
 
2.2%
Other values (3) 698186
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11401056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3813943
33.5%
D 1266784
 
11.1%
E 1266784
 
11.1%
T 1266784
 
11.1%
1 936704
 
8.2%
2 689036
 
6.0%
3 533594
 
4.7%
4 398401
 
3.5%
5 274831
 
2.4%
8 256009
 
2.2%
Other values (3) 698186
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11401056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3813943
33.5%
D 1266784
 
11.1%
E 1266784
 
11.1%
T 1266784
 
11.1%
1 936704
 
8.2%
2 689036
 
6.0%
3 533594
 
4.7%
4 398401
 
3.5%
5 274831
 
2.4%
8 256009
 
2.2%
Other values (3) 698186
 
6.1%

VDS_ID
Text

Distinct125
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:19.744031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters8867488
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVDS0029
2nd rowVDS0049
3rd rowVDS0061
4th rowVDS0035
5th rowVDS0035
ValueCountFrequency (%)
vds0023 17280
 
1.4%
vds0005 17274
 
1.4%
vds0059 14400
 
1.1%
vds0055 14400
 
1.1%
vds0028 14400
 
1.1%
vds0029 14395
 
1.1%
vds0022 14390
 
1.1%
vds0077 14385
 
1.1%
vds0087 14385
 
1.1%
vds0098 14380
 
1.1%
Other values (115) 1117095
88.2%
2024-03-26T09:38:20.109062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2547159
28.7%
V 1266784
14.3%
D 1266784
14.3%
S 1266784
14.3%
1 581519
 
6.6%
2 333851
 
3.8%
7 245163
 
2.8%
8 244489
 
2.8%
5 230245
 
2.6%
6 226570
 
2.6%
Other values (3) 658140
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8867488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2547159
28.7%
V 1266784
14.3%
D 1266784
14.3%
S 1266784
14.3%
1 581519
 
6.6%
2 333851
 
3.8%
7 245163
 
2.8%
8 244489
 
2.8%
5 230245
 
2.6%
6 226570
 
2.6%
Other values (3) 658140
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8867488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2547159
28.7%
V 1266784
14.3%
D 1266784
14.3%
S 1266784
14.3%
1 581519
 
6.6%
2 333851
 
3.8%
7 245163
 
2.8%
8 244489
 
2.8%
5 230245
 
2.6%
6 226570
 
2.6%
Other values (3) 658140
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8867488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2547159
28.7%
V 1266784
14.3%
D 1266784
14.3%
S 1266784
14.3%
1 581519
 
6.6%
2 333851
 
3.8%
7 245163
 
2.8%
8 244489
 
2.8%
5 230245
 
2.6%
6 226570
 
2.6%
Other values (3) 658140
 
7.4%
Distinct142
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:20.287090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters15201408
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVL1850000032
2nd rowVL1840000053
3rd rowVL1880000067
4th rowVL1850000038
5th rowVL1850000038
ValueCountFrequency (%)
vl1860000026 17280
 
1.4%
vl1860000007 17274
 
1.4%
vl1830000065 14400
 
1.1%
vl1850000031 14400
 
1.1%
vl1850000032 14395
 
1.1%
vl1860000025 14390
 
1.1%
vl1880000113 14380
 
1.1%
vl1880000115 14380
 
1.1%
vl1860000008 14380
 
1.1%
vl1880000095 14375
 
1.1%
Other values (132) 1117130
88.2%
2024-03-26T09:38:20.561597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6263477
41.2%
8 2180135
 
14.3%
1 1747022
 
11.5%
V 1266784
 
8.3%
L 1266784
 
8.3%
2 579212
 
3.8%
5 489245
 
3.2%
3 356204
 
2.3%
6 341154
 
2.2%
7 281990
 
1.9%
Other values (2) 429401
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15201408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6263477
41.2%
8 2180135
 
14.3%
1 1747022
 
11.5%
V 1266784
 
8.3%
L 1266784
 
8.3%
2 579212
 
3.8%
5 489245
 
3.2%
3 356204
 
2.3%
6 341154
 
2.2%
7 281990
 
1.9%
Other values (2) 429401
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15201408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6263477
41.2%
8 2180135
 
14.3%
1 1747022
 
11.5%
V 1266784
 
8.3%
L 1266784
 
8.3%
2 579212
 
3.8%
5 489245
 
3.2%
3 356204
 
2.3%
6 341154
 
2.2%
7 281990
 
1.9%
Other values (2) 429401
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15201408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6263477
41.2%
8 2180135
 
14.3%
1 1747022
 
11.5%
V 1266784
 
8.3%
L 1266784
 
8.3%
2 579212
 
3.8%
5 489245
 
3.2%
3 356204
 
2.3%
6 341154
 
2.2%
7 281990
 
1.9%
Other values (2) 429401
 
2.8%

DAY_CLS
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
1
1266784 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1266784
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1266784
100.0%

Length

2024-03-26T09:38:20.745513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-26T09:38:20.881860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1266784
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1266784
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1266784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1266784
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1266784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1266784
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1266784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1266784
100.0%

DETR_FAIL_YN
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
0
1266775 
3
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1266784
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1266775
> 99.9%
3 9
 
< 0.1%

Length

2024-03-26T09:38:21.025709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-26T09:38:21.157495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1266775
> 99.9%
3 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 1266775
> 99.9%
3 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1266784
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1266775
> 99.9%
3 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1266784
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1266775
> 99.9%
3 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1266784
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1266775
> 99.9%
3 9
 
< 0.1%

TR_VOL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5924838
Minimum0
Maximum110
Zeros630544
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:21.282902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum110
Range110
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5177467
Coefficient of variation (CV)1.5810187
Kurtosis12.225642
Mean1.5924838
Median Absolute Deviation (MAD)1
Skewness2.4277347
Sum2017333
Variance6.3390485
MonotonicityNot monotonic
2024-03-26T09:38:21.462716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 630544
49.8%
1 229281
 
18.1%
2 127657
 
10.1%
3 79794
 
6.3%
4 54988
 
4.3%
5 38967
 
3.1%
6 28558
 
2.3%
7 21321
 
1.7%
8 15965
 
1.3%
9 12209
 
1.0%
Other values (28) 27500
 
2.2%
ValueCountFrequency (%)
0 630544
49.8%
1 229281
 
18.1%
2 127657
 
10.1%
3 79794
 
6.3%
4 54988
 
4.3%
5 38967
 
3.1%
6 28558
 
2.3%
7 21321
 
1.7%
8 15965
 
1.3%
9 12209
 
1.0%
ValueCountFrequency (%)
110 1
< 0.1%
101 1
< 0.1%
85 1
< 0.1%
70 1
< 0.1%
61 1
< 0.1%
50 1
< 0.1%
47 1
< 0.1%
43 1
< 0.1%
40 1
< 0.1%
31 1
< 0.1%

SM_TR_VOL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1863277
Minimum0
Maximum33
Zeros715634
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:21.615987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0358248
Coefficient of variation (CV)1.7160728
Kurtosis7.8467613
Mean1.1863277
Median Absolute Deviation (MAD)0
Skewness2.5428303
Sum1502821
Variance4.1445826
MonotonicityNot monotonic
2024-03-26T09:38:21.756163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 715634
56.5%
1 226774
 
17.9%
2 115834
 
9.1%
3 68828
 
5.4%
4 44840
 
3.5%
5 30417
 
2.4%
6 20929
 
1.7%
7 14325
 
1.1%
8 10261
 
0.8%
9 7006
 
0.6%
Other values (15) 11936
 
0.9%
ValueCountFrequency (%)
0 715634
56.5%
1 226774
 
17.9%
2 115834
 
9.1%
3 68828
 
5.4%
4 44840
 
3.5%
5 30417
 
2.4%
6 20929
 
1.7%
7 14325
 
1.1%
8 10261
 
0.8%
9 7006
 
0.6%
ValueCountFrequency (%)
33 1
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
21 2
 
< 0.1%
20 2
 
< 0.1%
19 9
 
< 0.1%
18 23
 
< 0.1%
17 58
 
< 0.1%
16 126
 
< 0.1%
15 327
< 0.1%

MD_TR_VOL
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35590282
Minimum0
Maximum18
Zeros1026404
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:21.902430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.99810581
Coefficient of variation (CV)2.8044336
Kurtosis30.314348
Mean0.35590282
Median Absolute Deviation (MAD)0
Skewness4.6693272
Sum450852
Variance0.99621522
MonotonicityNot monotonic
2024-03-26T09:38:22.122093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 1026404
81.0%
1 144250
 
11.4%
2 48013
 
3.8%
3 21349
 
1.7%
4 10978
 
0.9%
5 6228
 
0.5%
6 3652
 
0.3%
7 2330
 
0.2%
8 1454
 
0.1%
9 893
 
0.1%
Other values (8) 1233
 
0.1%
ValueCountFrequency (%)
0 1026404
81.0%
1 144250
 
11.4%
2 48013
 
3.8%
3 21349
 
1.7%
4 10978
 
0.9%
5 6228
 
0.5%
6 3652
 
0.3%
7 2330
 
0.2%
8 1454
 
0.1%
9 893
 
0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 9
 
< 0.1%
15 10
 
< 0.1%
14 41
 
< 0.1%
13 98
 
< 0.1%
12 172
 
< 0.1%
11 342
 
< 0.1%
10 560
 
< 0.1%
9 893
0.1%
8 1454
0.1%

LG_TR_VOL
Real number (ℝ)

SKEWED  ZEROS 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050251661
Minimum0
Maximum110
Zeros1232613
Zeros (%)97.3%
Negative0
Negative (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:22.301240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum110
Range110
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47894159
Coefficient of variation (CV)9.5308609
Kurtosis5587.239
Mean0.050251661
Median Absolute Deviation (MAD)0
Skewness40.55926
Sum63658
Variance0.22938505
MonotonicityNot monotonic
2024-03-26T09:38:22.427763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 1232613
97.3%
1 24515
 
1.9%
2 3961
 
0.3%
3 1879
 
0.1%
4 1060
 
0.1%
5 684
 
0.1%
6 527
 
< 0.1%
7 372
 
< 0.1%
8 316
 
< 0.1%
9 276
 
< 0.1%
Other values (23) 581
 
< 0.1%
ValueCountFrequency (%)
0 1232613
97.3%
1 24515
 
1.9%
2 3961
 
0.3%
3 1879
 
0.1%
4 1060
 
0.1%
5 684
 
0.1%
6 527
 
< 0.1%
7 372
 
< 0.1%
8 316
 
< 0.1%
9 276
 
< 0.1%
ValueCountFrequency (%)
110 1
< 0.1%
101 1
< 0.1%
85 1
< 0.1%
70 1
< 0.1%
61 1
< 0.1%
50 1
< 0.1%
47 1
< 0.1%
43 1
< 0.1%
31 1
< 0.1%
29 1
< 0.1%

TRVL_SPD
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct206
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.082826
Minimum0
Maximum227
Zeros630544
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:22.601384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q355
95-th percentile76
Maximum227
Range227
Interquartile range (IQR)55

Descriptive statistics

Standard deviation30.612413
Coefficient of variation (CV)1.0900759
Kurtosis-0.45329385
Mean28.082826
Median Absolute Deviation (MAD)10
Skewness0.56979673
Sum35574875
Variance937.11981
MonotonicityNot monotonic
2024-03-26T09:38:22.793148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 630544
49.8%
54 18994
 
1.5%
55 18829
 
1.5%
56 18557
 
1.5%
52 18553
 
1.5%
53 18536
 
1.5%
57 18301
 
1.4%
51 18099
 
1.4%
50 17745
 
1.4%
58 17735
 
1.4%
Other values (196) 470891
37.2%
ValueCountFrequency (%)
0 630544
49.8%
1 46
 
< 0.1%
2 47
 
< 0.1%
3 135
 
< 0.1%
4 169
 
< 0.1%
5 964
 
0.1%
6 309
 
< 0.1%
7 308
 
< 0.1%
8 330
 
< 0.1%
9 385
 
< 0.1%
ValueCountFrequency (%)
227 1
 
< 0.1%
225 1
 
< 0.1%
220 1
 
< 0.1%
216 1
 
< 0.1%
201 13
 
< 0.1%
200 714
0.1%
199 2
 
< 0.1%
198 3
 
< 0.1%
197 3
 
< 0.1%
196 5
 
< 0.1%

OCCUPY_RATE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5207
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0802075
Minimum0
Maximum100
Zeros624936
Zeros (%)49.3%
Negative0
Negative (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:22.974398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.44
Q33.33
95-th percentile12.23
Maximum100
Range100
Interquartile range (IQR)3.33

Descriptive statistics

Standard deviation8.3465006
Coefficient of variation (CV)2.7097202
Kurtosis88.726438
Mean3.0802075
Median Absolute Deviation (MAD)0.44
Skewness8.3843934
Sum3901957.6
Variance69.664073
MonotonicityNot monotonic
2024-03-26T09:38:23.184017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 624936
49.3%
1.33 16774
 
1.3%
1.22 16464
 
1.3%
1.44 15997
 
1.3%
1.11 14601
 
1.2%
1.55 12309
 
1.0%
1 11996
 
0.9%
1.89 11439
 
0.9%
1.78 11328
 
0.9%
2 10762
 
0.8%
Other values (5197) 520178
41.1%
ValueCountFrequency (%)
0 624936
49.3%
0.1 92
 
< 0.1%
0.11 70
 
< 0.1%
0.12 55
 
< 0.1%
0.13 35
 
< 0.1%
0.14 26
 
< 0.1%
0.15 212
 
< 0.1%
0.16 44
 
< 0.1%
0.17 64
 
< 0.1%
0.18 57
 
< 0.1%
ValueCountFrequency (%)
100 5977
0.5%
99.42 1
 
< 0.1%
99.29 1
 
< 0.1%
99.27 1
 
< 0.1%
99.23 1
 
< 0.1%
98.42 1
 
< 0.1%
98.22 1
 
< 0.1%
98.1 1
 
< 0.1%
97.8 1
 
< 0.1%
97.76 1
 
< 0.1%

AVG_CAR_LEN
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct256
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.127243
Minimum0
Maximum255
Zeros633053
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:23.332687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q349
95-th percentile107
Maximum255
Range255
Interquartile range (IQR)49

Descriptive statistics

Standard deviation44.716374
Coefficient of variation (CV)1.3498369
Kurtosis4.5550465
Mean33.127243
Median Absolute Deviation (MAD)1
Skewness1.8869528
Sum41965061
Variance1999.5541
MonotonicityNot monotonic
2024-03-26T09:38:23.475519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 633053
50.0%
91 104533
 
8.3%
49 45345
 
3.6%
50 39516
 
3.1%
46 26974
 
2.1%
45 21051
 
1.7%
47 20430
 
1.6%
44 18536
 
1.5%
48 17769
 
1.4%
43 16850
 
1.3%
Other values (246) 322727
25.5%
ValueCountFrequency (%)
0 633053
50.0%
1 465
 
< 0.1%
2 257
 
< 0.1%
3 517
 
< 0.1%
4 347
 
< 0.1%
5 509
 
< 0.1%
6 309
 
< 0.1%
7 587
 
< 0.1%
8 299
 
< 0.1%
9 471
 
< 0.1%
ValueCountFrequency (%)
255 361
< 0.1%
254 150
 
< 0.1%
253 291
< 0.1%
252 233
< 0.1%
251 434
< 0.1%
250 174
< 0.1%
249 329
< 0.1%
248 176
< 0.1%
247 389
< 0.1%
246 150
 
< 0.1%

AVG_CAR_TM
Real number (ℝ)

ZEROS 

Distinct241
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.789598
Minimum0
Maximum254
Zeros430682
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size9.7 MiB
2024-03-26T09:38:23.634155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q330
95-th percentile60
Maximum254
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation18.138137
Coefficient of variation (CV)1.0803199
Kurtosis2.2547614
Mean16.789598
Median Absolute Deviation (MAD)10
Skewness1.1305037
Sum21268794
Variance328.992
MonotonicityNot monotonic
2024-03-26T09:38:23.982962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 430682
34.0%
30 396499
31.3%
60 88871
 
7.0%
15 68561
 
5.4%
10 46358
 
3.7%
7 35598
 
2.8%
3 31930
 
2.5%
6 28523
 
2.3%
5 23726
 
1.9%
4 21137
 
1.7%
Other values (231) 94899
 
7.5%
ValueCountFrequency (%)
0 430682
34.0%
1 1182
 
0.1%
2 18373
 
1.5%
3 31930
 
2.5%
4 21137
 
1.7%
5 23726
 
1.9%
6 28523
 
2.3%
7 35598
 
2.8%
8 6817
 
0.5%
9 5285
 
0.4%
ValueCountFrequency (%)
254 1
 
< 0.1%
248 1
 
< 0.1%
247 1
 
< 0.1%
245 4
< 0.1%
244 2
< 0.1%
243 1
 
< 0.1%
242 2
< 0.1%
241 2
< 0.1%
240 1
 
< 0.1%
239 2
< 0.1%

ETL_TYPE
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
602
1266784 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3800352
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row602
2nd row602
3rd row602
4th row602
5th row602

Common Values

ValueCountFrequency (%)
602 1266784
100.0%

Length

2024-03-26T09:38:24.119670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-26T09:38:24.226763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
602 1266784
100.0%

Most occurring characters

ValueCountFrequency (%)
6 1266784
33.3%
0 1266784
33.3%
2 1266784
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3800352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1266784
33.3%
0 1266784
33.3%
2 1266784
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3800352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1266784
33.3%
0 1266784
33.3%
2 1266784
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3800352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1266784
33.3%
0 1266784
33.3%
2 1266784
33.3%

ETL_DATE
Categorical

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2020-01-20 03:33:51
 
54048
2020-01-20 03:34:32
 
50422
2020-01-20 03:34:17
 
50276
2020-01-20 03:34:39
 
48578
2020-01-20 03:34:11
 
48543
Other values (44)
1014917 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters24068896
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-01-20 03:33:51
2nd row2020-01-20 03:33:51
3rd row2020-01-20 03:33:51
4th row2020-01-20 03:33:51
5th row2020-01-20 03:33:51

Common Values

ValueCountFrequency (%)
2020-01-20 03:33:51 54048
 
4.3%
2020-01-20 03:34:32 50422
 
4.0%
2020-01-20 03:34:17 50276
 
4.0%
2020-01-20 03:34:39 48578
 
3.8%
2020-01-20 03:34:11 48543
 
3.8%
2020-01-20 03:34:42 48127
 
3.8%
2020-01-20 03:34:21 47058
 
3.7%
2020-01-20 03:34:00 45780
 
3.6%
2020-01-20 03:34:29 45218
 
3.6%
2020-01-20 03:34:26 43989
 
3.5%
Other values (39) 784745
61.9%

Length

2024-03-26T09:38:24.334042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-01-20 1266784
50.0%
03:33:51 54048
 
2.1%
03:34:32 50422
 
2.0%
03:34:17 50276
 
2.0%
03:34:39 48578
 
1.9%
03:34:11 48543
 
1.9%
03:34:42 48127
 
1.9%
03:34:21 47058
 
1.9%
03:34:00 45780
 
1.8%
03:34:29 45218
 
1.8%
Other values (40) 828734
32.7%

Most occurring characters

ValueCountFrequency (%)
0 6654312
27.6%
2 4172258
17.3%
3 2959618
12.3%
- 2533568
 
10.5%
: 2533568
 
10.5%
1 1678817
 
7.0%
4 1441933
 
6.0%
1266784
 
5.3%
5 359755
 
1.5%
9 168683
 
0.7%
Other values (3) 299600
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24068896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6654312
27.6%
2 4172258
17.3%
3 2959618
12.3%
- 2533568
 
10.5%
: 2533568
 
10.5%
1 1678817
 
7.0%
4 1441933
 
6.0%
1266784
 
5.3%
5 359755
 
1.5%
9 168683
 
0.7%
Other values (3) 299600
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24068896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6654312
27.6%
2 4172258
17.3%
3 2959618
12.3%
- 2533568
 
10.5%
: 2533568
 
10.5%
1 1678817
 
7.0%
4 1441933
 
6.0%
1266784
 
5.3%
5 359755
 
1.5%
9 168683
 
0.7%
Other values (3) 299600
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24068896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6654312
27.6%
2 4172258
17.3%
3 2959618
12.3%
- 2533568
 
10.5%
: 2533568
 
10.5%
1 1678817
 
7.0%
4 1441933
 
6.0%
1266784
 
5.3%
5 359755
 
1.5%
9 168683
 
0.7%
Other values (3) 299600
 
1.2%

Interactions

2024-03-26T09:38:13.196079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:58.071961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:59.986807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:02.254248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:05.144061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:07.468228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:09.258365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:11.245702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:13.425464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:58.280606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:00.251176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:02.566200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:05.573485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:07.690632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:09.483696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:11.463443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:13.671036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:58.504243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:00.498433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:03.165318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:05.851741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:07.904361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:09.792321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:11.745491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:13.957386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:58.736821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:00.741680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:03.674505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:06.169152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:08.113067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:10.017607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:11.997322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:14.225547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:58.964190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:01.021726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:04.005285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:06.481283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:08.359683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:10.238335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:12.233561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:14.451385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:59.225341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:01.364567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:04.270441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:06.717270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:08.583467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:10.464595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:12.451054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:14.701086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:59.510960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:01.634015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:04.537979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:06.995696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:08.807894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:10.691124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:12.682460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:14.906201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:37:59.746937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:01.953574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:04.873773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:07.250520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:09.032497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:10.990808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-26T09:38:12.903573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-26T09:38:24.438200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AVG_CAR_LENAVG_CAR_TMDETR_FAIL_YNETL_DATELG_TR_VOLMD_TR_VOLOCCUPY_RATESM_TR_VOLTRVL_SPDTR_VOL
AVG_CAR_LEN1.000-0.1050.0130.1210.1910.4130.8370.7430.8570.851
AVG_CAR_TM-0.1051.0000.0000.062-0.029-0.023-0.085-0.116-0.063-0.098
DETR_FAIL_YN0.0130.0001.0000.0100.016-0.0010.005-0.0010.0010.005
ETL_DATE0.1210.0620.0101.0000.0370.1020.2270.2210.1600.228
LG_TR_VOL0.191-0.0290.0160.0371.0000.0390.1990.0240.1360.182
MD_TR_VOL0.413-0.023-0.0010.1020.0391.0000.5690.3070.4490.564
OCCUPY_RATE0.837-0.0850.0050.2270.1990.5691.0000.8550.7910.961
SM_TR_VOL0.743-0.116-0.0010.2210.0240.3070.8551.0000.7600.908
TRVL_SPD0.857-0.0630.0010.1600.1360.4490.7910.7601.0000.853
TR_VOL0.851-0.0980.0050.2280.1820.5640.9610.9080.8531.000

Missing values

2024-03-26T09:38:15.216347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-26T09:38:16.526432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

REG_YMDHMSDETR_IDVDS_IDVDS_SECTN_IDDAY_CLSDETR_FAIL_YNTR_VOLSM_TR_VOLMD_TR_VOLLG_TR_VOLTRVL_SPDOCCUPY_RATEAVG_CAR_LENAVG_CAR_TMETL_TYPEETL_DATE
02020-01-19 00:40:44DET002905VDS0029VL18500000321000000.00.000.030.06022020-01-20 03:33:51
12020-01-19 00:40:44DET004902VDS0049VL18400000531000000.00.000.030.06022020-01-20 03:33:51
22020-01-19 00:40:44DET006102VDS0061VL18800000671000000.00.000.00.06022020-01-20 03:33:51
32020-01-19 00:40:45DET003501VDS0035VL18500000381000000.00.000.030.06022020-01-20 03:33:51
42020-01-19 00:40:45DET003503VDS0035VL185000003810202084.02.7858.015.06022020-01-20 03:33:51
52020-01-19 00:40:45DET004001VDS0040VL18300000431000000.00.000.030.06022020-01-20 03:33:51
62020-01-19 00:40:45DET004003VDS0040VL18300000441000000.00.000.030.06022020-01-20 03:33:51
72020-01-19 00:40:45DET005502VDS0055VL18500000591000000.00.000.030.06022020-01-20 03:33:51
82020-01-19 00:40:45DET005504VDS0055VL18500000601000000.00.000.030.06022020-01-20 03:33:51
92020-01-19 00:40:46DET002201VDS0022VL186000002510110056.01.3339.030.06022020-01-20 03:33:51
REG_YMDHMSDETR_IDVDS_IDVDS_SECTN_IDDAY_CLSDETR_FAIL_YNTR_VOLSM_TR_VOLMD_TR_VOLLG_TR_VOLTRVL_SPDOCCUPY_RATEAVG_CAR_LENAVG_CAR_TMETL_TYPEETL_DATE
12667742020-01-19 09:43:46DET002204VDS0022VL186000002510211059.02.6748.015.06022020-01-20 03:34:21
12667752020-01-19 09:43:46DET002601VDS0026VL185000002910110038.01.5540.030.06022020-01-20 03:34:21
12667762020-01-19 09:43:46DET002603VDS0026VL18500000291000000.00.000.030.06022020-01-20 03:34:21
12667772020-01-19 09:43:46DET004902VDS0049VL18400000531000000.00.000.030.06022020-01-20 03:34:21
12667782020-01-19 09:43:46DET006102VDS0061VL188000006710440058.04.6791.010.06022020-01-20 03:34:21
12667792020-01-19 09:43:46DET008601VDS0086VL188000009710440057.04.78192.013.06022020-01-20 03:34:21
12667802020-01-19 09:43:46DET008603VDS0086VL18800000981000000.00.000.00.06022020-01-20 03:34:21
12667812020-01-19 09:43:46DET009601VDS0096VL188000011110110084.01.1191.00.06022020-01-20 03:34:21
12667822020-01-19 09:43:46DET009603VDS0096VL188000011110101044.03.4456.00.06022020-01-20 03:34:21
12667832020-01-19 09:43:47DET004502VDS0045VL185000004910320144.04.9022.010.06022020-01-20 03:34:21